Open AccessApplication of Recommendation Medical Specialty Doctors Based on User Symptoms Using the C4.5 Method and K-Nearest Neighbor
Author(s) -
Hendri,
Viny Christanti Mawardi,
Dali Santun Naga
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1007/1/012152
Subject(s) - specialty , k nearest neighbors algorithm , computer science , sample (material) , process (computing) , information retrieval , data mining , medicine , family medicine , artificial intelligence , chemistry , chromatography , operating system
This paper is about web based application of recommendation medical specialty doctors based on user symptoms using the C4.5 method and K-Nearest Neighbor. Classification is the process of grouping things based on classes with the characteristics of similarities and differences. This objects or entities are labeled as user symptoms. User symptoms are classified and produce a category of doctor that matches the user’s symptoms. This application will be using sample data from ascertain the opinions and experience of Doctor Yohannes Cahyadi who practices at the Griya Kasih Indah Clinic and from the website alodokter, hellosehat, docdoc.com, cicendoeyehospital, and the eye clinic nusantara. The purpose of making this application is to choose the right specialist doctor based on human symptoms or medical need. The test results obtained said that the best classification accuracy is to use the K-Nearest Neighbor method with an accuracy value of 100%, this shows that the use of the C4.5 method and K-Nearest Neighbor can provide recommendations for the right doctor with user symptoms.